Biomarkers and methods for detecting alzheimer's disease

Abstract

Methods for classifying a test sample as indicative of Alzheimer's disease use protein and peptide biomarkers that are differentially expressed in the cerebral spinal fluid (CSF) of subjects with Alzheimer's disease relative to age-matched controls. The methods also use protein and peptide signatures indicative of Alzheimer's disease. Microarrays and kits for detecting the protein and peptide biomarkers in CSF samples can be used to classify Alzheimer's disease state from test samples.

1 . A method of classifying Alzheimer's disease state of a subject, comprising: a) providing a test sample from the subject; b) determining expression levels in the test sample of at least one protein or peptide biomarker selected from any of the biomarkers set out in TABLES 2A, 2B or 5, or determining expression levels in the test sample of the proteins or peptides comprising any one of the biomarker combinations set out in TABLES 3B, 3C, 4B, or 4C; c) classifying the levels of expression of the selected biomarkers relative to expression levels of the biomarkers in a reference tissue sample as altered or not altered; and d) classifying the test sample according to (c), wherein altered expression levels of the biomarkers in the tissue sample relative to expression levels of the biomarkers in the reference sample indicate a classification of Alzheimer's disease (AD) in the subject. 
     
     
         2 . The method of  claim 1 , wherein the tissue sample comprises a cerebral spinal fluid sample. 
     
     
         3 . The method of  claim 1 , wherein the biomarkers are any one or more of the biomarkers selected from any one of Tables 2A, Table 2B and 5. 
     
     
         4 . The method of  claim 1 , wherein the biomarkers consist of an optimal set of biomarkers as set forth in any one of Tables 3B, 3C, 4B and 4C. 
     
     
         5 . A method for classifying Alzheimer's disease (AD) state of a subject, comprising: a) selecting a statistically relevant multi-analyte panel from fluid samples obtained from human subjects including a control cohort consisting of healthy subjects and an AD cohort consisting of subjects diagnosed with AD, in which panel a plurality of protein or peptide biomarkers are differentially expressed to provide expression values for a reference AD panel and a control panel; b) conducting a Random Forests or Simulated Annealing analysis on the multi-analyte data from step (a) to derive a signature; c) applying a classification algorithm to the signature of step (b) to refine the signature; d) obtaining a test fluid sample from the subject; e) determining expression level in the test sample for each of the protein biomarkers used to specify the panel of (a); e) comparing the results of step (e) to the signature obtained from step (c) to obtain an output; and f) determining the classification of the disease state according to the output of step e), wherein the classification is either AD or control. 
     
     
         6 . The method of  claim 5 , wherein the classification algorithm in (c) is selected from: Linear Discriminant Analysis (LDA), Diagonal Linear Discriminant Analysis (DLDA), Diagonal Quadratic Discriminant Analysis (DQDA), Random Forests, Support Vector Machines, Neural Network, and k-Nearest Neighbor method. 
     
     
         7 . The method of  claim 5 , wherein the multi-analyte panel consists of an optimal panel as set forth in Table 3B, and has at least 72% sensitivity and at least 71% specificity for Alzheimer's disease. 
     
     
         8 . The method of  claim 5 , wherein the multi-analyte panel consists of an optimal panel as set forth in Table 3C, and has at least 60% sensitivity and at least 80% specificity for Alzheimer's disease. 
     
     
         9 . The method of  claim 5 , wherein the multi-analyte panel consists of an optimal panel as set forth in Table 4B, and has at least 78% sensitivity and at least 90% specificity for Alzheimer's disease. 
     
     
         10 . The method of  claim 5 , wherein the multi-analyte panel consists of an optimal panel as set forth in Table 4C, and has at least 76% sensitivity and at least 90% specificity for Alzheimer's disease. 
     
     
         11 . A computer-implemented method for classifying a test sample obtained from a subject, comprising: (a) obtaining a dataset associated with the test sample, wherein the obtained dataset comprises quantitative data for at least one protein or peptide biomarker selected from any of the biomarkers set out in TABLES 2A, 2B or 5, or the obtained dataset comprises quantitative data for the biomarkers comprising any one of the biomarker combinations as set out in TABLES 3B, 3C, 4B, or 4C; (b) inputting the obtained dataset into an analytical process on a computer that compares the obtained dataset against one or more reference datasets; and (c) classifying the test sample according to the output of the analytical process, wherein the classification is selected from the group consisting of an Alzheimer's disease (AD) classification and a normal classification. 
     
     
         12 . The method of  claim 11 , wherein the test sample is spinal fluid. 
     
     
         13 . The method of  claim 11 , wherein the protein or peptide biomarkers comprise anoptimal panel selected from a multi-analyte panel consisting of any one of the biomarker combinations set out in TABLES 3B, 3C, 4B, or 4C. 
     
     
         14 . The method of  claim 11 , wherein the analytical process comprises applying to the obtained dataset either Random Forests or Simulated Annealing algorithm to derive optimal signatures, and applying at least one algorithm selected from: Linear Discriminant Analysis (LDA), Diagonal Linear Discriminant Analysis (DLDA), Diagonal Quadratic Discriminant Analysis (DQDA), Support Vector Machines, Neural Network, and k-Nearest Neighbor method to fit the classification model on the optimal signatures. 
     
     
         15 . A computer system comprising: (a) a database containing information identifying the expression level in spinal fluid of a set of genes encoding at least two proteins or peptide biomarkers set out in any one of TABLES 2A, 2B, 3B, 3C, 4B, 4C and 5; and b) a user interface to view the information. 
     
     
         16 . A kit for classifying a test sample obtained from a human subject, comprising reagents for detecting at least one protein or peptide biomarker selected from any one of the biomarkers set out in TABLES 2A, 2B or 5, or reagents for detecting any one of the protein or peptide biomarker combinations as set out in any one of TABLES 3B, 3C, 4B, or 4C. 
     
     
         17 . A biomarker indicative of AD selected from any one of Tables 2A, 2B, 3B, 3C, 4B, 4C and 5. 
     
     
         18 . An array of primers or probes for classifying one or more test samples for Alzheimer's disease state, the array comprising: at least two different primers or probes coupled to a solid support; wherein each primer or probe is capable of specifically hybridizing under stringent conditions to a protein or peptide biomarker according to  claim 17 . 
     
     
         19 . The array of  claim 18 , wherein the biomarkers are any one or more biomarkers selected from any of TABLES 2A, 2B and 5 having an altered expression level of each biomarker between the AD disease state and control that is at a q-value of <0.1, or any two or more biomarkers selected from TABLES 2A, 2B and 5 having an altered expression level of each biomarker between the AD disease state and control that is at a p-value of <0.05. 
     
     
         20 . An isolated peptide having an amino acid sequence selected from the group consisting of SEQ ID NO: 111, SEQ ID NO: 112, SEQ ID NO: 114, SEQ ID NO: 121, SEQ ID NO: 124, and SEQ ID NO: 126.